Prediction and association analyses of skin phenotypes in Japanese females using genetic, environmental, and physical features.

Abstract

Skin characteristics show great variation from person to person and are affected by multiple factors, including genetic, environmental, and physical factors, but details of the involvement and contributions of these factors remain unclear. We aimed to characterize genetic, environmental, and physical factors affecting 16 skin features by developing models to predict personal skin characteristics. We analyzed the associations of skin phenotypes with genetic, environmental, and physical features in 1472 Japanese females aged 20-80 years. We focused on 16 skin characteristics, including melanin, brightness/lightness, yellowness, pigmented spots, wrinkles, resilience, moisture, barrier function, texture, and sebum amount. As genetic factors, we selected 74 single-nucleotide polymorphisms of genes related to skin color, vitamin level, hormones, circulation, extracellular matrix (ECM) components and ECM-degrading enzymes, inflammation, and antioxidants. Histories of ultraviolet (UV) exposure and smoking as environmental factors and age, height, and weight as physical factors were acquired by means of a questionnaire. A linear association with age was prominent for increase in the area of crow's feet, increase in number of pigmented spots, decrease in forehead sebum, and increase in VISIA wrinkle parameters. Associations were analyzed by constructing linear regression models for skin feature changes and logistic regression models to predict whether subjects show lower or higher skin measurement values in the same age groups. Multiple genetic factors, history of UV exposure and smoking, and body mass index were statistically selected for each skin characteristic. The most important association found for skin spots, such as lentigines and wrinkles, was adolescent sun exposure. Genetic, environmental, and physical factors associated with interindividual differences of the selected skin features were identified. The developed models should be useful to predict the skin characteristics of individuals and their age-related changes.